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Hyperpod Nccl

Organization
awslabs
hyperpod-nccl

Diagnose NCCL failures and adjacent training-pod failures on HyperPod GPU clusters (EKS or Slurm) — training hangs, AllReduce / collective-op timeouts, EFA or libfabric errors, rendezvous failures, EFA TCP fallback, /dev/shm or memlock issues, NCCL version mismatch across pods, container OOM / exit-137 / OOMKilled, GPU OOM (CUDA out of memory), CrashLoopBackOff / Pending pods, MASTER_ADDR DNS, NetworkPolicy blocking. Not for single-node hardware faults (→ hyperpod-node-debugger § G) or cluster-creation EFA / SSM failures (→ hyperpod-cluster-debugger § A / § F).

Overview

Publisherawslabs
Repositoryagent-plugins
Skill namehyperpod-nccl
Stars
917
Forks
162
Bundled files
5
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by awslabs on GitHub. Read the source before you install it.

Installation

Install the Hyperpod Nccl AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/hyperpod-nccl
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/awslabs/agent-plugins.git /tmp/agent-plugins
mkdir -p .claude/skills
cp -r /tmp/agent-plugins/plugins/sagemaker-ai/skills/hyperpod-nccl .claude/skills/hyperpod-nccl
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hyperpod Nccl in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hyperpod Nccl on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hyperpod Nccl is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

HyperPod NCCL Debugger

Operating policy. Run read-only diagnostics yourself. Never run a command that changes cluster, node, or workload state — present each one as a Suggested command (run this yourself) block and wait for the customer. Destructive order: investigate → reboot → replace (replace destroys root + secondary volumes; not supported on Slurm controller nodes). Never discard training state on speculation.

Diagnose NCCL failures on SageMaker HyperPod (EKS and Slurm). scripts/nccl-diagnose.sh reads state via AWS APIs, kubectl, and SSM, then prints each issue as [FAIL] ... → references/<file>.md § <section>. Read-only.

Signal sourcing: list-cluster-events carries infrastructure-level state only (lifecycle, bootstrap, EFA health check, capacity, replacement, reboot, AMI rollback). It does not carry NCCL timeouts, GPU XID/ECC, or per-pod training signals — those come from pod logs, CloudWatch training streams, on-node SSM probes, and NCCL env audit. "No events" on a training-time NCCL issue is expected, not a clean bill of health.


Workflow

  1. Collect cluster name, region, namespace/job (EKS), exact NCCL error string.
  2. Run the diagnostic (always — the output drives everything else).
  3. For every [FAIL] line, Read the referenced section.
  4. Present finding, root cause, and the Suggested-command block with concrete values (instance IDs, SG IDs, namespaces) filled in from the script output. Wait for customer approval.
  5. Re-run the diagnostic to confirm.

If a finding has no matching section, report it as a bug — do not invent a fix.

Step 1: Authenticate kubectl (EKS)

bash
EKS_ARN=$(aws sagemaker describe-cluster --cluster-name <HYPERPOD-NAME> --region <REGION> \
  --query 'Orchestrator.Eks.ClusterArn' --output text)
EKS_NAME=$(echo "$EKS_ARN" | awk -F'/' '{print $NF}')
aws eks update-kubeconfig --name "$EKS_NAME" --region <REGION>
kubectl get nodes

Step 2: Run the diagnostic

bash
# Basic:
bash scripts/nccl-diagnose.sh --cluster <HYPERPOD-NAME> --region <REGION>

# Scope to an EKS job/namespace:
bash scripts/nccl-diagnose.sh --cluster <NAME> --region <REGION> --namespace <NS> --job <JOB>

# Force orchestrator:
bash scripts/nccl-diagnose.sh --cluster <NAME> --region <REGION> --orchestrator slurm

# Larger hardware sample (default 3):
bash scripts/nccl-diagnose.sh --cluster <NAME> --region <REGION> --sample-nodes 10

# Specific node only:
bash scripts/nccl-diagnose.sh --cluster <NAME> --region <REGION> --node i-0abc123def456

Tags: [PASS] · [FAIL] (counted in Issues Found, has reference pointer) · [WARN] · [INFO]. Priorities: P0 blocks training · P1 degraded · P2 informational.


Remediation index

Each [FAIL] line in the script already points directly at the right section. This table is a lookup for manual triage.

FindingSection
SG missing inbound/outbound self-referenceoperations.md § 8
Blocking NetworkPolicy / allow-all missingoperations.md § 8
Slurm node DOWN / DRAINING / RemoveIPCoperations.md § 7
GPU XID / SYSTEM_ERROR / hardware faulthyperpod-node-debugger § F / § G
GPU row-remap / DCGM Fail / silent NaNshyperpod-node-debugger § G.1.a/b
NCCL timeout / rendezvous / stragglerdebugging-guide.md § 1
EFA configuration / not useddebugging-guide.md § 6
EFA TCP fallback (NET/OFI Using TCP)debugging-guide.md § 13
NCCL version mismatch across podsdebugging-guide.md § 10
Container OOM (pod killed, exit 137)debugging-guide.md § 4
GPU OOM (CUDA out of memory)debugging-guide.md § 11
RDMA memlock / /dev/shm too smalldebugging-guide.md § 17
MASTER_ADDR DNS / headless Servicedebugging-guide.md § 12
NVLS / PXN / topology tuningdebugging-guide.md § 19
Any NCCL / EFA / rendezvous log patternerror-patterns-quick-ref.md
Performance / nccl-tests / bandwidthperformance-testing.md

Prerequisites

  • aws CLI v2.13+ authenticated (aws sts get-caller-identity)
  • jq, python3, bash 4.2+
  • unbuffer (from the expect package: yum install expect / apt install expect)
  • kubectl authenticated to the EKS cluster (K8s checks skipped if absent)
  • session-manager-plugin for on-node hardware checks

Defaults

  • Region — required: pass --region or set $AWS_DEFAULT_REGION.
  • Orchestrator — auto-detected; override with --orchestrator eks|slurm.
  • Namespace / job (EKS) — all namespaces; scope with --namespace <NS> --job <JOB>.
  • Hardware sampling — 3 nodes over SSM (capped at 50). --node <ID> for a specific node. Node probes run serially (180 s per node): --sample-nodes 10 can take ~30 min.
  • CloudWatch window — last 2 hours.
  • Colors — auto-disabled on non-TTY or TERM=dumb.

Error handling

FailureScriptTell the customer
aws sts get-caller-identity failsExit 1 with the AWS error"Fix AWS credentials and rerun."
describe-cluster AccessDeniedWarn, add Missing IAM for sagemaker:DescribeCluster"Grant sagemaker:DescribeCluster (operations.md § 2)."
Cluster not foundExit 1 after listing region's clusters"Confirm HyperPod cluster name and region."
kubectl absent / unauthenticatedWarn, skip K8s checks"aws eks update-kubeconfig --name <EKS> --region <R>."
SSM plugin absentWarn, skip on-node hardware checks"Install session-manager-plugin."
SSM times out (180s)Partial output, mark node unreachable"Rerun with --node <ID> --sample-nodes 1; check SSM agent on the node."
CloudWatch log group not foundSkip CloudWatch scan"Enable CloudWatch on the cluster (operations.md § 4)."
Cluster events API throttledWarn, continue with partial data"Rerun later — script is idempotent."

Exit codes: 0 diagnostic complete · 1 fatal prerequisite missing or cluster unreachable.

IAM permissions

Full policy + RBAC in operations.md § 2. SSM on HyperPod uses start-session against sagemaker-cluster:<cluster-id>_<group>-<iid> targets — grant ssm:StartSession / ssm:TerminateSession, not ssm:SendCommand.

Scale strategy

ScopeMethodCoverage
All nodessagemaker:ListClusterNodes (paginated)100% nodes
All K8s objectskubectl100% pods/nodes/policies
HardwareSSM --sample-nodes N (default 3)Sampled
Node logsCloudWatch100% nodes

Large clusters: the PyTorch NCCL backend defaults to a 10-minute collective-op timeout (per the PyTorch distributed docs). Large clusters routinely exceed that on first rendezvous; raise it via torch.distributed.init_process_group(timeout=timedelta(seconds=<N>)). HyperPod support has also observed NCCL topology-graph-search hangs on 256+ node clusters when memlock is unlimited; using a large fixed memlock (e.g. 8388608) in pod securityContext or /etc/security/limits.conf has cleared these in field cases. This memlock pattern is a field observation, not AWS- or NCCL-documented behavior.

For FSDP, DeepSpeed, or Megatron-LM tuning: debugging-guide.md § 18.

Skill delegation

NeedUse
Cluster creation / deployment failureshyperpod-cluster-debugger (§ A / B / C / H + --validate)
Post-deployment cluster-wide managementhyperpod-cluster-debugger
Per-node issues (disk, lifecycle, hardware)hyperpod-node-debugger
Trainium/Inferentia collective-comm (AWS Neuron Collectives, not NCCL)hyperpod-node-debugger § G.2
Shell on nodeshyperpod-ssm
Version comparison across nodeshyperpod-version-checker
Diagnostic bundle for AWS Supporthyperpod-issue-report
MFU / performance degradationhyperpod-mfu-debugger

Escalate to AWS Support

Escalate when:

  1. All SG rules correct, EFA verified on-node, but NCCL still times out.
  2. Hardware checks pass on all nodes but AllReduce still hangs.
  3. Issues Found: 0 but training still fails.
  4. GPU XID errors persist after node replacement.
  5. Collective-op timeout raised and memlock workaround applied but large-cluster rendezvous still hangs.

Before opening the case

bash
# 1. Cluster identity + status
aws sagemaker describe-cluster --cluster-name <C> --region <R>

# 2. Full NCCL diagnostic (sample more nodes for escalation)
bash scripts/nccl-diagnose.sh --cluster <C> --region <R> --sample-nodes 10 > nccl-diag.txt

# 3. Per-node log/config bundle to S3 (delegates to hyperpod-issue-report)
#    See skills/hyperpod-issue-report/SKILL.md for the exact invocation.

Include in the case

  • Cluster name + ARN and AWS region
  • Orchestrator (EKS or Slurm) and EKS cluster name / Slurm controller node
  • Timestamp window (UTC start / end) of the failure
  • Exact NCCL / libfabric error strings (copy verbatim from pod logs or journalctl)
  • Affected instance IDs / node names / pod names / namespace / job name
  • nccl-diag.txt from step 2 above
  • S3 URI of the hyperpod-issue-report bundle from step 3
  • NCCL env vars in effect (printenv | grep -E '^NCCL|^FI_|^TORCH_' from one pod)

References

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Hyperpod Nccl AI skill do?

Diagnose NCCL failures and adjacent training-pod failures on HyperPod GPU clusters (EKS or Slurm) — training hangs, AllReduce / collective-op timeouts, EFA or libfabric errors, rendezvous failures, EFA TCP fallback, /dev/shm or memlock issues, NCCL version mismatch across pods, container OOM / exit-137 / OOMKilled, GPU OOM (CUDA out of memory), CrashLoopBackOff / Pending pods, MASTER_ADDR DNS, NetworkPolicy blocking. Not for single-node hardware faults (→ hyperpod-node-debugger § G) or cluster-creation EFA / SSM failures (→ hyperpod-cluster-debugger § A / § F).

Why use Hyperpod Nccl on TypingMind?

Because you install it once and use it with any model. Hyperpod Nccl is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hyperpod Nccl in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/hyperpod-nccl. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Hyperpod Nccl?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hyperpod Nccl?

As many as you like. As long as a model supports skills, you can use Hyperpod Nccl with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hyperpod Nccl AI skill free?

Yes. It is published on GitHub by awslabs under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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